Water Quality Parameters Research Articles

Modular stochastic configuration network with attention mechanism for soft measurement of water quality parameters in wastewater treatment processes

Due to the complex and multi-distributed nature of wastewater treatment process data, stochastic configuration networks (SCNs) face difficulties such as large network structure and complex construction process when dealing with data modeling tasks. To address these challenges, this paper proposes a modular stochastic configuration network with attention mechanism, which integrates the advantages of modular partitioning, attention mechanism, and SCN. The model employs a fuzzy mean clustering strategy to simplify and decompose the complex multi-case and multi-distribution nonlinear modeling task, thereby reducing the modeling complexity of a single model. Furthermore, the model utilizes a stochastic configuration algorithm to learn the subtasks and construct corresponding sub-networks. The output weights are then assigned to each sub-network based on the attention mechanism to obtain the model’s final output. The validity of the proposed method is validated through two benchmark experiments. Then the model is applied to measure effluent ammonia nitrogen. The results indicate that compared with the classical randomized and modularized network models, the proposed model exhibits certain advantages in terms of learning accuracy and generalization performance.

Tracing pesticide dynamics: High resolution offers new insights to karst groundwater quality

Generally, karst aquifers and springs are highly susceptible to contamination due to the high permeability and, therefore, groundwater flow velocities. The often thin soil cover, accompanied by dolines, can lead to fast infiltration of precipitation water loaded with mobilized contaminants such as pesticides and their transformation products. To date, continuous, temporally highly resolved in-situ monitoring to decipher concentration dynamics for a broad range of pesticides is missing. Therefore, a transportable HPLC-HRMS/MS system (MS2field) was positioned at two karst study sites in the Swiss Jura. Water samples were collected and analyzed for pesticides and their transformation products in-situ every 20 min for 6 weeks in 2021 and 8 weeks in 2022. During the spraying season in 2021, six rain events at site 1 and three at site 2 in 2022 were captured. Concurrently, the water quality parameters electrical conductivity, pH, nitrate, turbidity, and water level, were monitored continuously at high temporal resolution. Further, bacterial cell counts were monitored via online flow cytometry.In 2021, several pesticides and pesticide transformation products were detected in peak concentrations after rain events, of which metamitron showed the highest concentration of up to 1000 ng/L. In one rain event, the Swiss federal and EU drinking water limit of 100 ng/L was exceeded for up to 38 h. Compared with highly frequent MS2field samples collected every 20 min, 42-hours composite samples severely underestimated peak concentrations for all compounds, especially for labile ones. Therefore, it was demonstrated that exceedences of the regulatory limit would have been missed if just composite sampling would have been conducted. Peak concentrations of pesticides coincided with peaks in nitrate concentration and bacterial cell counts following rain events. The correlation analysis showed strong correlations between the three analyzed contaminants (pesticides, nitrate and bacteria), and the proxy parameters electrical conductivity, and pH. The investigation of a second spring revealed similar dynamics indicating that these can be expected in other karst aquifers as well.

Canadian beach cohort study: protocol of a prospective study to assess the burden of recreational water illness

BackgroundRecreational water activities at beaches are popular among Canadians. However, these activities can increase the risk of recreational water illnesses (RWI) among beachgoers. Few studies have been conducted in Canada to determine the risk of these illnesses. This protocol describes the methodology for a study to determine the risk and burden of RWI due to exposure to fecal pollution at beaches in Canada.MethodsThis study will use a mixed-methods approach, consisting of a prospective cohort study of beachgoers with embedded qualitative research. The cohort study involves recruiting and enrolling participants at public beaches across Canada, ascertaining their water and sand contact exposure status, then following-up after seven days to determine the incidence of acute RWI outcomes. We will test beach water samples each recruitment day for culture-based E. coli, enterococci using rapid molecular methods, and microbial source tracking biomarkers. The study started in 2023 and will continue to 2025 at beaches in British Columbia, Manitoba, Ontario, and Nova Scotia. The target enrollment is 5000 beachgoers. Multilevel logistic regression models will be fitted to examine the relationships between water and sand contact and RWI among beachgoers. We will also examine differences in risks by beachgoer age, gender, and beach location and the influence of fecal indicator bacteria and other water quality parameters on these relationships. Sensitivity analyses will be conducted to examine the impact of various alternative exposure and outcome definitions on these associations. The qualitative research phase will include focus groups with beachgoers and key informant interviews to provide additional contextual insights into the study findings. The study will use an integrated knowledge translation approach.DiscussionInitial implementation of the study at two Toronto, Ontario, beaches in 2023 confirmed that recruitment is feasible and that a high completion rate (80%) can be achieved for the follow-up survey. While recall bias could be a concern for the self-reported RWI outcomes, we will examine the impact of this bias in a negative control analysis. Study findings will inform future recreational water quality guidelines, policies, and risk communication strategies in Canada.

Urbanization and Suspended Sediment Transport Dynamics: A Comparative Study of Watersheds with Varying Degree of Urbanization Using Concentration-Discharge Hysteresis.

Suspended sediment is a critical water quality parameter and an indicator of geomorphic processes, but suspended sediment dynamics in urban streams may not conform to the first-flush model widely used for other pollutants. We analyzed discharge and turbidity data for 367 events from three urban watersheds (impervious cover 16-45%) in Cleveland, Ohio (USA). Less intensely urbanized watersheds exhibit higher turbidity compared to that of the most highly urbanized watershed. Proportionally, more counterclockwise hysteresis is observed in the two less urbanized watersheds, and more clockwise hysteresis occurs in the highly urbanized watershed. However, hysteresis patterns are driven by different mechanisms in each watershed, and geomorphic analysis was critical to identifying the underlying mechanisms. In the least urbanized watershed, spatial rainfall variability controls sediment hysteresis. In the intermediate watershed, the erosion of upstream weathered shale banks during dry periods plays a significant role in the sediment supply and shaping hysteresis. In the most urbanized watershed, high eroding banks in downstream reaches lead to more frequent clockwise hysteresis. Overall, we suggest that as the impervious surfaces increase, the availability of instream sediments (bed and banks) plays an increased role in suspended sediment dynamics, and geomorphology remains essential for guiding management decisions.

Microfluidic Platform for Semiconductor and MOF Integrated Photocatalysts: A Review over Synthesis Approaches and Applications

AbstractThe synergy of integrating semiconductors and MOF photocatalysts is the chief motive for this review paper. This merit has been combined with the microfluidic platforms, which are the cutting‐edge technology of synthesis. The microfluidic synthesis of semiconductors and MOF photocatalysts are categorized according to morphology and linker, respectively. Enhancing the effectiveness of photocatalysis comes along with adding a proper amount of electron acceptor or hole scavenger, combing electrochemical with photocatalytic degradation, and optimizing the photocatalyst band gap. Moreover, approaches for improving photocatalytic activity of MOFs can be classified as functionalization of organic linkers/metal centers, sensitization, incorporation of nanoparticles, and hybridization with semiconductors. Finally, the current applications of microfluidic devices, from the detection of water quality parameters to the elimination of water contaminants, have been addressed.

Seasonal dynamics and hydrogeological assessment of Najafgarh drain and its irrigation channels for agricultural purposes in Southwest Delhi, India

ABSTRACT This research paper focuses on evaluating the suitability of drain water, whether untreated or treated, for irrigation purposes in a peri-urban region of southwest Delhi, India. A total of 11 drain water samples were collected during the pre-monsoon (PRM) and post-monsoon (POM) seasons. A comprehensive assessment of various water quality parameters, including pH, EC, major cations (Na+, Ca2+, Mg2+, K+), anions (Cl−, HCO3−, SO42−, NO3−, F−), heavy metals, and irrigation water quality (SAR, Na%, RSC, KR, MH, PI) were analyzed. The results revealed that the drain water was highly unsuitable and marginally suitable for irrigation in PRM and POM. The Wilcox diagram classified 91% of samples as doubtful to unsuitable during PRM but only 63% during POM due to the rainfall. Heavy metal concentrations often exceeded permissible limits, with Fe ranging from 0.98 to 8.65 ppm (PRM) and 0.004 to 10.32 ppm (POM). Other metals like Mn (0.12–0.98 ppm PRM), Zn (0.02–2.54 ppm POM), Cd (0–0.031 ppm POM), and Cu (0.07–0.345 ppm POM) also showed elevated levels. The chemical composition of the water was influenced by evaporation and rock–water interactions as indicated by Gibbs diagrams in both the seasons.

Assessment of water quality and identification of priority areas for intervention in Guanabara Bay basin, Rio de Janeiro, Brazil, using nonparametric and multivariate statistical methods.

The Guanabara Bay hydrographic region (GBHR) has served as a central hub for human settlement and resource utilization throughout Brazil's history. However, the region's high population density and intense industrial activity have come at a cost, leading to a significant decline in water quality. This work aimed to identify homogeneous regions in GBHR according to water quality parameters in dry and rainy periods. The following water quality monitoring variables were monitored at 49 gauge stations: total phosphorus (TP), nitrate (NO3-), dissolved oxygen (DO), hydrogenionic potential (pH), turbidity (Turb), thermotolerant coliforms (TCol), total dissolved solids (TDS), biochemical oxygen demand (BOD), water temperature (Tw), and air temperature (Ta). The statistical analysis consisted of determining principal components, cluster analysis, seasonal differences, and Spearman's correlation. The water quality parameter correlations were not expressively influenced by seasonality, but there are differences in the concentrations of these parameters in the dry and rainy periods. In the dry period, urban pressure on water quality is mainly due to fecal coliforms. The resulting clusters delimited areas under urban, agricultural, and forestry influence. Clusters located in areas with high demographic density showed high concentrations of TCol and TP, while clusters influenced by forestry and agriculture had better water quality. In the rainy season, clusters with urban influence showed problems with TCol and TP, in addition to some characteristics in each group, such as high TDS, NO3-, and BOD. Forested areas showed high DO, and clusters under agricultural influence had higher concentrations of TCol, BOD, and NO3- concerning forested regions. The troubling state of sanitation in GBHR occurs in metropolitan regions due to lack of a formal sanitation system.

Assessing the effects of Water hyacinth proliferation on biochemical oxygen demand with operational land imager data

The rapid proliferation of water hyacinth (Eichhornia crassipes) in water bodies poses a significant threat to ecosystems and communities that rely on these resources. While this plant can offer certain benefits, its excessive growth can lead to detrimental ecological and social impacts. This study aimed to assess the relationship between water hyacinth coverage and Biochemical Oxygen Demand (BOD) levels in the Sonso Lagoon, Colombia, using Landsat 8 satellite imagery and in-situ measurements. Water hyacinth coverage was classified and quantified from Landsat 8 images. These data were then correlated with BOD levels obtained through periodic monitoring of the lagoon. The analysis revealed a strong negative correlation (ρ=−0.83; p−value<0.05**) between water hyacinth coverage and BOD, demonstrating the plant's impact on water quality. A predictive model was developed to estimate BOD levels based on satellite-derived water hyacinth data. The integration of remote sensing and in-situ measurements offers an effective strategy for monitoring water hyacinth proliferation and its ecological consequences. The proposed approach enables rapid assessment of water quality parameters, facilitating timely implementation of mitigation and control measures. This methodology can be extended to quantify other invasive aquatic plant species and their impacts on aquatic ecosystems.

Exploration of a model driven by climate to simulate pond water temperature in aquaculture systems

IntroductionInterannual climate variability in the Asian mega deltas has been posing a wide range of climate risks in the aquaculture systems of the region. Water temperature variation is one of the key risks related to disease outbreak, fish health, and loss and damage in fish production. However, Climate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Little research has been done to translate weather data into water temperature forecasts using mechanistic models in order to provide farmers with relevant forecasting information in the context of climate services.MethodsThe advantage of mechanistic models over statistical models is that they are based on physical processes and can therefore be used in a wider range of environmental conditions. In this study, we used an energy balance model to investigate its ability to simulate pond water temperature at daily and seasonal timescales in the southwest and northeast regions of Bangladesh. The model was able to adequately simulate pond water temperature at a daily timescale using publicly available weather data, and the accuracy of the model was lower at the study site with very heavy rainfall events.ResultsSensitivity analyses showed that the model was also able to simulate the impact of air temperature cold and hot spells on the pond water temperature. Connecting the model with seasonal air temperature forecasts resulted in very small variations in the forecasted seasonal pond water temperature, in large part due to the low variability observed in water temperature at seasonal scale in the study sites.DiscussionClimate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Hence, these improved predictions are important to help fish-farmers make informed decisions for managing associated climate risks.

Impact of Various Dietary Protein Sources on Growth Performance of Nile Tilapia (Oreochromis niloticus) Fingerlings and on Water Quality

Aquaculture industry provides proteins and unsaturated fatty acids worldwide. Fresh water aquaculture is considered as the most prominent type of aquaculture in the world. Nile tilapia aquaculture is favored because it can adopt to wide range of environments and can tolerate various abiotic stresses. However, the success and profitability of aquaculture is significantly affected by type of feeds used in aquaculture system. An experiment was conducted to assess this important aspect of aquaculture. Nile tilapia fingerlings were fed on three different protein sources to assess the effects of these experimental diets on growth of fingerlings and on water quality. Three protein sources were freshwater shrimp meal (FSM), black soldier fly larvae (BSF) and soybean meal (SBM). Fingerlings were divided into groups based on their respective experimental diets and were arranged according to CRD with three replicates. Each replicate contained 30 fingerlings. Data was recorded for growth attributes of fingerlings and for water quality attributes. Analysis of data shows that BSF caused 32% increase in final weight of fingerlings followed by FSM, which caused 24% increase as compared to controlled. Net weight gain was also significantly enhanced by experimental diets. BSF caused 88% increase in weight gain as compared to controlled. BSF also increased 43% survival rate of Nile tilapia fingerlings as compared to fingerlings fed with controlled diet. Water quality was not much affected by all diets. Water quality parameters like DO, pH, temperature, ammonia and phosphorus all were in optimum range for tilapia culture. However, BSF provided 136% more phosphorus as compared to controlled. This study reveals the beneficial effects of BSF larvae protein source for Nile tilapia culture. So, from these findings, it can be suggested to partially replace fish meal with BSF larvae meal.

Impact of Various Dietary Protein Sources on Growth Performance of Nile Tilapia (Oreochromis niloticus) Fingerlings and on Water Quality

Aquaculture industry provides proteins and unsaturated fatty acids worldwide. Fresh water aquaculture is considered as the most prominent type of aquaculture in the world. Nile tilapia aquaculture is favored because it can adopt to wide range of environments and can tolerate various abiotic stresses. However, the success and profitability of aquaculture is significantly affected by type of feeds used in aquaculture system. An experiment was conducted to assess this important aspect of aquaculture. Nile tilapia fingerlings were fed on three different protein sources to assess the effects of these experimental diets on growth of fingerlings and on water quality. Three protein sources were freshwater shrimp meal (FSM), black soldier fly larvae (BSF) and soybean meal (SBM). Fingerlings were divided into groups based on their respective experimental diets and were arranged according to CRD with three replicates. Each replicate contained 30 fingerlings. Data was recorded for growth attributes of fingerlings and for water quality attributes. Analysis of data shows that BSF caused 32% increase in final weight of fingerlings followed by FSM, which caused 24% increase as compared to controlled. Net weight gain was also significantly enhanced by experimental diets. BSF caused 88% increase in weight gain as compared to controlled. BSF also increased 43% survival rate of Nile tilapia fingerlings as compared to fingerlings fed with controlled diet. Water quality was not much affected by all diets. Water quality parameters like DO, pH, temperature, ammonia and phosphorus all were in optimum range for tilapia culture. However, BSF provided 136% more phosphorus as compared to controlled. This study reveals the beneficial effects of BSF larvae protein source for Nile tilapia culture. So, from these findings, it can be suggested to partially replace fish meal with BSF larvae meal.

Integrated Geospatial and Geostatistical Multi-Criteria Evaluation of Urban Groundwater Quality Using Water Quality Indices

Groundwater contamination poses a severe public health risk in Lahore, Pakistan’s second-largest city, where over-exploited aquifers are the primary municipal and domestic water supply source. This study presents the first comprehensive district-wide assessment of groundwater quality across Lahore using an innovative integrated approach combining geographic information systems (GIS), multi-criteria decision analysis (MCDA), and water quality indexing techniques. The core objectives were to map the spatial distributions of critical pollutants like arsenic, model their impacts on overall potability, and evaluate targeted remediation scenarios. The analytic hierarchy process (AHP) methodology was applied to derive weights for the relative importance of diverse water quality parameters based on expert judgments. Arsenic received the highest priority weight (0.28), followed by total dissolved solids (0.22) and hardness (0.15), reflecting their significance as health hazards. Weighted overlay analysis in GIS delineated localized quality hotspots, unveiling severely degraded areas with very poor index values (&gt;150) in urban industrial zones like Lahore Cantt, Model Town, and parts of Lahore City. This corroborates reports of unregulated industrial effluent discharges contributing to aquifer pollution. Prospective improvement scenarios projected that reducing heavy metals like arsenic by 30% could enhance quality indices by up to 20.71% in critically degraded localities like Shalimar. Simulating advanced multi-barrier water treatment processes showcased an over 95% potential reduction in arsenic levels, indicating the requirement for deploying advanced oxidation and filtration infrastructure aligned with local contaminant profiles. The integrated decision support tool enables the visualization of complex contamination patterns, evaluation of remediation options, and prioritizing risk-mitigation investments based on the spatial distribution of hazard exposures. This framework equips urban planners and utilities with critical insights for developing targeted groundwater quality restoration policies through strategic interventions encompassing treatment facilities, drainage infrastructure improvements, and pollutant discharge regulations. Its replicability across other regions allows for tackling widespread groundwater contamination challenges through robust data synthesis and quantitative scenario modeling capabilities.

Impacts of fish pond effluent on the water quality of an afrotropical stream: a comprehensive evaluation using the water quality index

This study highlights the importance of managing fishpond effluent to minimize its impact on aquatic environments. Fish effluents are a major concern worldwide in aquatic environments. It also provides information on the impacts of effluents on the water quality index of streams when released into aquatic environments. Streams and fishponds were sampled bimonthly for two annual cycles, covering different sampling stations both upstream and downstream of the fishpond effluents at 50-m intervals. Water quality parameters associated with in situ conditions were determined in the field. In the laboratory, other physicochemical parameters were determined via proper standard protocols. The results of the study revealed that water quality parameters such as turbidity, total suspended solids, total solids, conductivity, total dissolved solids, dissolved oxygen, biological oxygen demand and organic matter recorded in water samples collected from 50 m upstream at the point of effluent discharge were significantly (p < 0.05) different from those recorded at other sampling stations. The WQI value of the discharge point was higher than the WQI recorded in both the upstream and downstream sections of the stream. The findings of this study showed that the point of discharge of fishpond effluents is particularly concerning for the receiving stream compared with other sampling points. This localized area has experienced marked deterioration in water quality, posing significant threats to the health and biodiversity of aquatic ecosystems. The effluent's impact, though slight when dispersed in the receiving waterbody, underscores the necessity for adequate management practices to mitigate its adverse effects. Effective strategies, including regular monitoring, effluent treatment, and sustainable pond management, are imperative to ensure the long-term health and resilience of the aquatic environment.

Water Quality Assessment and its Impact on Shrimp in the Rupnarayan River: A Comparative Study from February to May of 2022-2024

The Rupnarayan River is the heart of the Purba Medinipur district of West Bengal, as it is the habitat of a large number of aquatic species and the main drinking, agricultural, irrigation, and cultivation resource. We collected river water samples from the Kolaghat, Alinan, Siuri and Deemari villages near Tamluk in the Purba Medinipur district. Then, we assessed different water quality parameters, such as temperature, turbidity, pH, DO, BOD, salinity, alkalinity, and hardness. We studied the premonsoon phase (Feb-May) and assessed the impact of these parameters on the production of shrimp species. We identified M. rosenbergi, M. brevicornis, M. monoceros, and Penaeus vannamei as shrimp species. We also compared the data from 2022 to 2024 (Feb-May) and observed yields of 40%, 60%, and 30% in 2022, 2023 and 2024, respectively.

Hyper-Parameter Optimization-based multi-source fusion for remote sensing inversion of non-photosensitive water quality parameters

ABSTRACT The constraints of spatiotemporal heterogeneity and spatial resolution constitute two crucial challenges in the establishment of remote sensing inversion models. Spatiotemporal heterogeneity gives rise to an inadequate generalization capacity of remote sensing models, demanding extensive manual parameter adjustment for each model construction. This not only escalates the task’s work intensity but also leads to unstable performance. The limited spatial resolution of remote sensing images leads to suboptimal inversion accuracy for sampling points influenced by mixed pixel effects. To tackle these problems, we take the case of non-photosensitive water quality parameter inversion in the narrow rivers of Longnan area. By integrating advanced Hyper-Parameter Optimization (HPO) techniques, such as Optuna from machine learning, an inversion model was developed, incorporating the bands of Sentinel-2 and Sentinel-3 as model features. Among these features, bands with lower spatial resolution are employed to furnish surrounding information, thereby enhancing the inversion accuracy. The research outcomes demonstrate that: 1) The model constructed based on the HPO method, Optuna, attained favourable inversion results, with R2 values of 0.68, 0.77, 0.35, and 0.60 for Total Nitrogen (TN), Total Phosphorus (TP), Ammonia Nitrogen (NH3-N), and Chemical Oxygen Demand (COD), respectively. 2) The fusion of Sentinel-2 and Sentinel-3 data enhanced the inversion accuracy compared to using them separately, highlighting the considerable significance of multi-source data fusion methods in improving inversion accuracy. This research fills a void in the remote sensing inversion domain and lays the groundwork for future endeavours.

Water Quality Assessment using Machine Learning: A Focus on Coliform Prediction in Water

Water quality assessment is essential for safeguarding public health and protecting water resources. This study focused on predicting water quality, specifically the presence of total coliforms, using various machine-learning techniques. The present study utilises a publicly available dataset encompassing the geographical area of India consisting of various physical water quality parameters. Various regression techniques were applied to the dataset after appropriate pre-processing including feature selection and normalisation. The findings demonstrate that gradient boosting regression outperforms other methods, achieving high accuracy with mean absolute error (MAE) of 0.0349, mean squared error (MSE) of 0.0038, and root mean squared error (RMSE) of 0.0620. Conductivity and temperature emerged as the most influential factors in total coliform prediction, as revealed by feature importance analysis. These results contribute to water quality understanding, aiding water resource management for public health protection. By accurately predicting total coliform presence, proactive measures can be taken timely to mitigate and minimise health risks associated with microbial contamination.

Comparative Effect of Fish Feeds on the Initial Growth and Survival Rate of Juvenile Redbreast Tilapia (Coptodon rendalli) under Early Hatchery Conditions

This study compared the effects of three supplementary diets—egg yolk, Artemia, and commercial pellets with 48% crude protein—on the growth performance and survival rate of redbreast tilapia (Coptodon rendalli) under intensive culture conditions at the Kamutjonga Inland Fisheries Institute in the Kavango East region of Namibia. The study was conducted from December 2023 to January 2024 using a complete randomized design replicated thrice. Results showed that fish fed with commercial pellets exhibited the highest specific growth rate (1.39 ± 0.80%) compared to egg yolk (0.94 ± 0.54%) and Artemia (0.33 ± 0.19%). Commercial pellets also had the best survival rate (76.19 ± 43.099), compared to egg yolk and Artemia (70.47 ± 40.69; 33 ± 19.05), respectively. However, egg yolk had the best feed conversion ratio (0.20 ± 0.05) compared to the commercial pellets (0.22 ± 0.04) and Artemia (0.26 ± 0.07). Critical water quality parameters were maintained within acceptable survival ranges for C. rendalli across all treatments. These findings highlight the importance of dietary protein content in optimizing the growth and survival of C. rendalli under intensive culture conditions and provide insights for effective feeding strategies. However, commercial pellets are expensive and inaccessible to small-scale fish farmers. As an alternative, egg yolk emerged as a viable and affordable feed option, promoting fish growth and supporting the establishment of small-scale farming practices in the region.

Obtaining and qualitative analysis of time-lagged correlations between seawater quality parameters

Abstract In the regulation of seawater quality, it is crucial to understand the interactions between parameters and the time-lagged effects. This paper focuses on the problem of how to obtain and analyze time-lagged correlations between seawater quality parameters, an issue that has not attracted the attention of researchers. In this paper, a graph neural network-based model, Dynamic Adjacency Weight Network, is proposed to model the time-lagged correlation between seawater quality parameters. By regarding the parameters as nodes, the proposed model retains the relationships between the parameters in a weighted adjacency matrix, overcoming the problem of traditional deep neural networks that are difficult to be visualized. Meanwhile, the proposed multivariate multistep shift prediction strategy enables the proposed model to accurately obtain the time-lagged correlation information between parameters at different time intervals. In addition, the proposed model also addresses some of the details worth considering when obtaining correlations between seawater quality parameters. The proposed model performs well in the prediction of water quality parameters. This indirectly verifies the validity of the obtained correlations and overcomes the difficulty of verifying the validity of statistical methods. This study provides new ideas and methods for seawater quality monitoring and research.

Impact of land use on shallow groundwater quality for domestic use in Mathira East Sub-County in Kenya

Shallow groundwater sources of boreholes and springs form major types of water resources used in rural areas of Kenya. Information on their status is inadequate, thus hindering their sustainable management. This study investigated the relationship between land-use types, groundwater sources and water quality. Thirty-six randomly selected groundwater sources were sampled in Mathira East Sub-County from four stratified land-use types of forest, tea, coffee, and urban, with 18 samples collected for either shallow boreholes or springs. The Principal Component Analyses (PCA) grouped groundwater resources into three groups according to groundwater sources and land-use types. Spearman rank correlation, one-way ANOVA and unpaired t-test found that land use types significantly influenced shallow groundwater quality along an increasing gradient of landscape disturbances from the less disturbed forest, then tea, coffee and lastly to the severely disturbed, the urban area. The most affected water parameters were turbidity, conductivity, NO3–N, phosphates and faecal coliforms and their variation was attributed to land-use changes, especially agricultural intensification and urbanisation. The significant variations in other water quality parameters (total hardness, total alkalinity, dissolved oxygen, pH, manganese and fluoride) were attributed to water–rock interactions, climatic factors and land use changes. Calculated Water Quality Index (WQI) with faecal coliforms parameter from groundwater sources were found unsuitable for domestic use, while the ones derived without faecal coliforms parameter were found safe for human use. Overall, shallow groundwater sources were contaminated and unsafe for human use. This study recommends regular monitoring of key water parameters affected by land use changes to support the sustainable management of shallow groundwater resources in Mathira East Sub-County and the larger region of South Sahara Africa.

Environmental Impact of River Bank Erosion on Stream Morphology: A Study of the Mini Piti Stream, Obio-Akpor, Nigeria

This study was designed to assess the impact of river bank erosion on the environment and the channel morphology for the Mini-Piti stream in Obio-Akpor local government area of Rivers State, Nigeria. This study determined the depth variations from width measurements due to bank erosion activities and the impact of indiscriminate waste dumps on the river channel. Physical water quality parameters of temperature, turbidity, transparency and TSS were determined using standard methods. The stream was divided into three stations; upstream 2.5km, midstream 2.5km, and downstream with intervals of 1km measured to differentiate each station as 5 samples were obtained per station at intervals of 0.5km apart. The flow velocity and discharge were estimated as the average depth recorded upstream being DS 0.9m, WS 1.4m, Midstream DS 1.3m, WS 1.7m, and Downstream = DS 1.5m WS 1.7m. The maximum width of 11m and minimum of 0.1m were recorded though natives confirmed that the stream is closing up unless maintenance dredging to expand the depth and width, the loss of this channel cannot exceed 20 years. The field observation and the impacts of precipitation and storm water from the built area of midstream were very intensive whose action increased the water level above its channel capacity and eroded the bank. From t-test analysis, there were significant seasonal variations in the depth for the measured widths for the upstream T (8) =2.1, p= 0.036 and the midstream, T (8) =2.36, p=0.023. Similarly using the one-ANOVA, a significant variation in the depths of five locations during the dry season downstream F (4, 60) = 2.79, p= .03 was recorded. The Tukey’s HSD post hoc test showed the locations have no significantly high depths for the studied widths sizes (p&lt;0.01 and p&lt;0.05). A one-way ANOVA revealed a significant variation in the depths of five locations during the wet season upstream F (4, 60) = 5.966, p= .000. The Tukey’s HSD post hoc test showed the locations have significantly high depths for the studied widths sizes (p&lt;0.01 and p&lt;0.05) at locations L2:L4 at Q=5.53 (p=0.00216, L2:L5 at Q= 4.72 (p=0.1214), L3:L4 at Q=4.90 (p=0.00841) and L3:L5 at Q=4.10 (p=0.0403). The studied physical parameters were all above WHO permissible standards and there were no significant variations between dry and wet seasons (38% turbidity midstream, TSS-33%, 31% and 30.6% variation upstream, midstream and downstream respectively) as well as spatial changes. The collapsed sediments and waste dumped at the bank of the stream were transported downstream due to reduced velocity and coarser sediments trapped at the stream bed causing shallow depth while the suspended fine sediment on the water column reduced the clarity of the stream resulting in adverse flooding over 20 years now. The research revealed that bank erosion has significant impact on water quality due to poor transparency and geometry of stream by the alterations of the channel depth and width, flow rate, scouring and deposition of sediments in the stream. The need for regulatory agencies to control anthropogenic inputs and enhance bank erosion prevention programmes cannot be over-emphasized. The monitoring of indiscriminate urbanization programmes to limit excess scouring activities by National Inland Waterways Authority (NIWA) is very essential to reduce excessive sand mining.